Science and Engineering 2, Room 302
CatCast Live link
Neural responses are variable: the same external events usually trigger different patterns of neural activity. These activity fluctuations have been traditionally treated as noise. However, recent experiments
have shown that they are not entirely random. For example, neural activity fluctuations are correlated with animal’s choices, and the strength of these fluctuations is modulated during spatial attention. Yet, it
remains largely unknown whether neural activity fluctuations play a functional role and how these fluctuations are controlled in service of behavior. In my talk, I will address both these questions. First, I will show that choice-correlated activity fluctuations play a critical role in rewarddependent learning. As an example, I consider categorization—an essential cognitive ability to group stimuli into discrete classes—and construct a biophysical model capable of learning categorization through reward-dependent plasticity. In the model, stable category representations develop in neurons intermediate to sensory and decision layers only if they exhibit choice-correlated activity fluctuations arising from plastic top-down projections. Specific model predictions are confirmed by analyses of recordings from monkey parietal cortex.
Second, I will present analyses of ensemble neural activity recorded with linear electrode arrays across layers in monkey visual cortex during a demanding attention task. This ensemble neural activity spontaneously transitions between episodes of vigorous (On) and weak (Off) spiking synchronous throughout the cortical depth. Classic work in rodents relates such synchronous transitions to global c hanges of cortical state associated with arousal. Using statistical modeling, I will demonstrate that On-Off dynamics in the primate cortex not only covary with arousal but are also modulated locally within a retinotopic map during spatial attention. Moreover, the instantaneous cortical state predicts animals’ behavioral responses, suggesting that local changes in the On-Off dynamics have direct behavioral consequences. These results elucidate the dynamics of neural activity fluctuations and demonstrate that cortical state is locally modulated to serve behavioral goals, challenging the traditional view of cortical state as a mere reflection of arousal.
Tatiana Engel is a postdoctoral fellow at Stanford University, working jointly with Prof. Kwabena Boahen and Prof. Tirin Moore. Her research aims to understand how behavioral and cognitive functions (such as attention, decision making, learning) arise from dynamics in large neural circuits, using a combination of theory, biophysical modeling and model-driven data analysis and in close collaboration with experimentalists. Before coming to Stanford, Tatiana was a postdoctoral fellow in the lab of Prof. Xiao-Jing Wang at Yale University, where she worked on building computational and neural circuit models of category learning, working memory and decision making. Prior to that, Tatiana obtained PhD in Theoretical Physics from Humboldt University of Berlin in Germany, and MSc in Physics from Lomonosov Moscow State University in Russia.
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